TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

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TensorFlow: a system for large-scale machine learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

TensorFlow: A system for large-scale machine learning

XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

http://db.ucsd.edu/static/cse232b-archive/papers/tatarinov02.pdf

Storing and querying ordered XML using a relational database system

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

With non-traditional application areas such as engineering design, image/voice data management, scientific/statistical applications, and artificial intelligence systems all clamoring for ways to store and efficiently process larger and larger volumes of data, it is clear that traditional database technology has been pushed to its limits. It also seems clear that no single database system will be capable of simultaneously meeting the functionality and performance requirements of such a diverse set of applications. In this paper we describe the preliminary design of EXODUS, an extensible database system that will facilitate the fast development of high-performance, application-specific database systems. EXODUS provides certain kernel facilities, including a versatile storage manager and a type manager. In addition, it provides an architectural framework for building application-specific database systems, tools to partially automate the generation of such systems, and libraries of software components (e.g., access methods) that are likely to be useful for many application domains.

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The architecture of the EXODUS extensible DBMS

A method for publishing relational data as XML by translating XML queries into queries against a relational database. Conversion of the relational database into an XML database is not required. Each relational table is mapped to a virtual XML document, and XML queries are issued over these virtual documents. An XML query is parsed and transformed into a language-neutral intermediate representation, which is a sequence of operations describing how the output document is derived from the underlying relational tables. The intermediate representation is then translated into an SQL query over the underlying relational tables. The intermediate representation is also used to generate a tagger graph, which the tagger runtime ‘walks’ to generate the tagged, structured XML output. Each of the nodes of the tagger graph are operators which perform processing on the results of the SQL query. The SQL query is executed, and the SQL query results are then provided to the tagger. The tagger runtime applies the operators of each node to the inputs at that node to produce the structured XML document as a query result, guided by the structure of the tagger graph.

Tagging XML query results over relational DBMSs

The Architecture of the EXODUS Extensible DBMS.

Query evaluation is optimized using parallel optimization techniques to make repartitioning more efficient. Efficiency is improved by recognizing the possible partitioning requirements for achieving parallelism for a query operation, and by recognizing when the partitioning property of data satisfies the partitioning requirements of a query operation. A data base management system in accordance with the invention uses parallel query processing techniques to optimize data repartitioning, or to avoid it altogether.

Optimization of data repartitioning during parallel query optimization

1 Currently at Propel, 2350 Mission College Blvd., Santa Clara, CA 95054. 2 Work done while the author was visiting the IBM Almaden Research Center from INRIA, Le Chesnay, France. 3 Work done while the author was visiting the IBM Almaden Research Center from the University of Washington, Seattle, WA 98155 4 Work done while the author was visiting the IBM Almaden Research Center from the University of Wisconsin, Madison, WI 53706. ABSTRACT Since its introduction, XML, the eXtended Markup Language, has quickly emerged as the universal format for publishing and exchanging data in the World Wide Web. As a result, data sources, including object-relational databases, are now faced with a new class of users: clients and customers who would like to deal directly with XML data rather than being forced to deal with the data source’s particular (e.g., object-relational) schema and query language. The goal of the XPERANTO project at the IBM Almaden Research Center is to serve as a middleware layer that supports the publishing of XML data to this class of users. XPERANTO provides a uniform, XML-based query interface over an object-relational database that allows users to query and (re)structure the contents of the database as XML data, ignoring the underlying SQL tables and query language. In this paper, we give an overview of the XPERANTO system prototype, explaining how it translates XML-based queries into SQL requests, receives and then structures the tabular query results, and finally returns XML documents to the system’s users and applications.

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Eugene J. Shekita

Papers

The architecture of the EXODUS extensible DBMS

Tagging XML query results over relational DBMSs

The Architecture of the EXODUS Extensible DBMS.

Optimization of data repartitioning during parallel query optimization

XPERANTO: Publishing Object-Relational Data as XML.